# On the Computation of the Efficient Frontier in Advanced Sparse Portfolio Optimization

arXiv: https://arxiv.org/abs/2501.19199 · HTML: https://mlsanity.com/paper/2501.19199/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2025.

## Built on by (most cited first, 1 of 1)

- [Sparsity Regularized and Robust Mean Variance Portfolio Selection Under Ellipsoidal Uncertainty](https://mlsanity.com/paper/2609.11749/) (2026)

## Abstract (arXiv)

In this work, we deal with the problem of computing a comprehensive front of efficient solutions in multi-objective portfolio optimization problems in presence of sparsity constraints. We start the discussion pointing out some weaknesses of the classical linear scalarization approach when applied to the considered class of problems. We are then motivated to propose a suitable algorithmic framework that is designed to overcome these limitations: the novel algorithm combines a gradient-based exploration-refinement strategy with a tailored initialization scheme based on memetic or multi-start descent procedures. Thorough computational experiments highlight how the proposed method is far superior to both linear scalarization and popular genetic algorithms.